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The goal of this paper is to develop an interactive web-based machine learning application to assist architects with multimodal inputs (sketches and textual information) for conceptual design. With different textual inputs, the application generates the architectural stylistic variations of a user’s initial sketch input as a design inspiration. A novel machine learning model for the multimodal input application is introduced and compared to others. The machine learning model is performed through procedural training with the content curation of training data (1) to control the fidelity of generated designs from the input and (2) to manage their diversity. The web-based interface is at its work in progress as a frontend of the proposed application for better user experience and future data collection. In this paper, the framework of the proposed interactive application is explained. Furthermore, the implementation of its prototype is demonstrated with various examples.

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Learning from examples with noisy labels has attracted increasing attention recently. But, this paper will show that the commonly used CIFAR-based datasets and the accuracy evaluation metric used in the literature are both inappropriate in this context. An alternative valid evaluation metric and new datasets are proposed in this paper to promote proper research and evaluation in this area. Then, friends and foes are identified from existing methods as technical components that are either beneficial or detrimental to deep learning from noisy labeled examples, respectively, and this paper improves and combines technical components from the friends category, including self-supervised learning, new warmup strategy, instance filtering and label correction. The resulting F&F method significantly outperforms existing methods on the proposed nCIFAR datasets and the real-world Cloth-ing1M dataset.